The purpose of this project is to conduct independent research in statistical methods and computer techniques with particular emphasis on those appropriate for analyzing data from clinical, diagnostic, and prevention trials and epidemiologic studies of cancer. Many of the problems studied under this project arise from the consultative activities of the Section. Among the current projects are: Analyzing Disease Progression with Status Measurements at Fixed Time Points: Use of Permutation Tests. In intervention trials in which patient status is measured regularly as an indication of disease progression, we often want an overall measure of impairment over time. Such measures are particularly of interest in the absence of a specific irreversible event whose incidence or time-to-occurrence can be compared between groups. Comparison of Methods for Identifying Prognostic Factors and Predicting Survival for Patients with Colorectal Cancer. In collaboration with a working group of the American Joint Committee on Cancer, work has continued on fitting Cox proportional hazards models to a dataset on patients with colorectal cancer. The goal is to identify important prognostic factors and then apply these to predict survival probabilities at various points of time. Methods for Analyzing Complex Survey Data. Data from household surveys are from clustered samples of persons who are often selected at differential rates. These aspects of the sampling result in nonindependence and unequal weighting of the observations that should be considered during the analysis stage. Survey data are used extensively in cohort studies through long term followup of the sample, case-control studies by providing population controls, and cross-sectional studies. Other projects involve collaboration with the Division of Cancer Treatment and the NCHS. Analysis of Diet Survey Data: Typical Consumption and Effects of Covariates. In collaboration with the University of Maryland, a parametric statistical model was constructed to model count data provided by 24-hour recall questionnaires. The model permits one to use regression models to relate abstention and average consumption to covariates such as income, race, day of week, or season. An important feature is that the model separates within and between person variation of count data. As a result, one can estimate the distribution of typical individual consumption, either for the entire population or for selected subpopulations. Interactive Statistical Programs. The Section continues to maintain and improve a group of interactive computer programs for efficient analysis of medical data, particularly those dealing with risk factors and prognostic factors using sophisticated multiple regression techniques and survival analysis.